Introduction to Hash Tables
Primarily in computer languages, hash tables implement data and map structures. Languages such as Python and Go feature pre-baked dictionary and map structures, while C++ and Java integrate them into their standard libraries. A hash table is a non-sequential data storing system that associates data keys with their corresponding values.
Comparative Performance of Data Structures
- Unsorted Array: In an unsorted array, the worst-case complexity for data lookup is linear.
- Sorted Array: A binary search in a sorted array offers high-speed item lookup but slows down while adding new items.
- Linked Lists: While linked lists allow for swift insertions, lookups demand linear time.
Hash Table Mechanics
A hash table stands out by arranging data in an array format, each item with a unique index value. If we know the data's id, access is significantly enhanced. Hash tables expedite the search and insert procedures by employing an array for storage and leveraging the hashing process to create an index for data insertion or location.
Hashing converts an array's key values into a chain of indexes in computing. The modulo operator is used to obtain acceptable values. This process is illustrated using a 20-byte hash table slated to host a sequence of values organized into keys and values.
Key Operations in Hash Tables
- Insert: Hash table insert function for inserting elements.
- Delete: Function for removing entries.
Handling Hash Collisions
Hash collision occurs when multiple keys generate the same index. Solutions include:
- Chaining: Resolves collision by forming a list of entries.
- Open Addressing: Each slot hosts a single key or remains vacant.
- Linear Probing: Manages collisions by inspecting consecutive slots.
- Quadratic Probing: Expands the gap between slots more than the usual single digit.
- Double Hashing: Applies a secondary hash function to determine where to place a key after a collision.
Resizing Hash Tables
Hash tables perform best when their sizes correlate with the number of records they host. Resizing is necessary when lists become too long, assuming the number of expected records is indefinite. If the table size is increased by a constant factor whenever resizing, such as doubling its size, it delivers compounded constant time for insertions.